Generative adversarial networks (GANs) can implicitly learn rich distributions over images, audio, and data which are hard to model with an explicit likelihood. The authors present a practical Bayesian formulation for unsupervised and semi-supervised learning with GANs. Within this framework, they use stochastic gradient Hamiltonian Monte Carlo to marginalize the weights of the generator and discriminator networks.